Add loss function and bool cast

modeling_mpt.py

@@ -56,7 +56,7 @@ class MPTModel(MPTPreTrainedModel):
             for module in self.modules():
                 if hasattr(module, 'bias') and isinstance(module.bias, nn.Parameter):
                     if config.verbose:
-                        print(f'Removing bias ({module.bias}) from {module}.')
+                        warnings.warn(f'Removing bias ({module.bias}) from {module}.')
                     module.register_parameter('bias', None)
         if config.verbose and config.verbose > 2:
             print(self)
@@ -131,6 +131,10 @@ class MPTModel(MPTPreTrainedModel):
     def forward(self, input_ids: torch.LongTensor, past_key_values: Optional[List[Tuple[torch.FloatTensor]]]=None, attention_mask: Optional[torch.ByteTensor]=None, prefix_mask: Optional[torch.ByteTensor]=None, sequence_id: Optional[torch.LongTensor]=None, return_dict: Optional[bool]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, use_cache: Optional[bool]=None):
         return_dict = return_dict if return_dict is not None else self.config.return_dict
         use_cache = use_cache if use_cache is not None else self.config.use_cache
+        if attention_mask is not None:
+            attention_mask = attention_mask.bool()
+        if prefix_mask is not None:
+            prefix_mask = prefix_mask.bool()
         if not return_dict:
             raise NotImplementedError('return_dict False is not implemented yet for MPT')
         if output_attentions:
@@ -228,7 +232,7 @@ class MPTForCausalLM(MPTPreTrainedModel):
     def get_decoder(self):
         return self.transformer
 
-    def forward(self, input_ids: torch.LongTensor, past_key_values: Optional[List[Tuple[torch.FloatTensor]]]=None, attention_mask: Optional[torch.ByteTensor]=None, prefix_mask: Optional[torch.ByteTensor]=None, sequence_id: Optional[torch.LongTensor]=None, return_dict: Optional[bool]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, use_cache: Optional[bool]=None):
+    def forward(self, input_ids: torch.LongTensor, past_key_values: Optional[List[Tuple[torch.FloatTensor]]]=None, attention_mask: Optional[torch.ByteTensor]=None, prefix_mask: Optional[torch.ByteTensor]=None, sequence_id: Optional[torch.LongTensor]=None, labels: Optional[torch.LongTensor]=None, return_dict: Optional[bool]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, use_cache: Optional[bool]=None):
         return_dict = return_dict if return_dict is not None else self.config.return_dict
         use_cache = use_cache if use_cache is not None else self.config.use_cache
         outputs = self.transformer(input_ids=input_ids, past_key_values=past_key_values, attention_mask=attention_mask, prefix_mask=prefix_mask, sequence_id=sequence_id, return_dict=return_dict, output_attentions=output_attentions, output_hidden_states=output_hidden_states, use_cache=use_cache)
@@ -237,7 +241,12 @@ class MPTForCausalLM(MPTPreTrainedModel):
             if self.logit_scale == 0:
                 warnings.warn(f'Multiplying logits by self.logit_scale={self.logit_scale!r}. This will produce uniform (uninformative) outputs.')
             logits *= self.logit_scale
-        return CausalLMOutputWithPast(logits=logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states)
+        loss = None
+        if labels is not None:
+            labels = torch.roll(labels, shifts=-1)
+            labels[:, -1] = -100
+            loss = F.cross_entropy(logits.view(-1, logits.size(-1)), labels.to(logits.device).view(-1))
+        return CausalLMOutputWithPast(loss=loss, logits=logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states)
 
     def param_init_fn(self, module):
         init_fn_name = self.config.init_config['name']